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Control, Automation and Information Sciences (ICCAIS), 2012 International Conference on

Date 26-29 Nov. 2012

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Displaying Results 1 - 25 of 76
  • [Copyright notice]

    Publication Year: 2012 , Page(s): 1
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    Publication Year: 2012 , Page(s): 1 - 8
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    Publication Year: 2012 , Page(s): 1 - 6
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    Publication Year: 2012 , Page(s): 1 - 2
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  • False-data attacks in stochastic estimation problems with only partial prior model information

    Publication Year: 2012 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (423 KB)  

    The security of state estimation in critical networked infrastructure such as the transportation and electricity (smart grid) networks is an increasingly important topic. Here, the problem of recursive estimation and model validation for linear discrete-time systems with partial prior information is examined. Further, detection of false-data attacks on robust recursive estimators of this type is considered. The framework considered in this work is stochastic. An underlying linear discrete-time system is considered where the statistics of the driving noise is assumed to be known only partially. A set-valued estimator is then derived and the conditional expectation is shown to belong to an ellipsoidal set consistent with the measurements and the underlying noise description. When the underlying noise is consistent with the underlying partial model and a sequence of realized measurements is given then the ellipsoidal, set-valued, estimate is computable using a Kalman filter-type algorithm. A group of attacking entities is then introduced with the goal of compromising the integrity of the state estimator by hijacking the sensor and distorting its output. It is shown that in order for the attack to go undetected, the distorted measurements need to be carefully designed. View full abstract»

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  • Control of a mobile sensor for multi-target tracking using multi-target/object Multi-Bernoulli filter

    Publication Year: 2012 , Page(s): 7 - 12
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (328 KB) |  | HTML iconHTML  

    In multi-object stochastic systems, the issue of sensor control is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem. Our approach is based on a partially observed Markov decision process (POMDP) where the reward function is a measure of information gain. The multi-target state is modelled as Multi-Bernoulli RFS, and the Multi-Bernoulli filter is used in conjunction with two different reward functions: maximizing the expected Rényi divergence between the predicted and updated densities, and minimizing the expected cardinality variance. Numerical studies and discussions are presented with range only measurements. View full abstract»

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  • Multiple cell tracking using ant estimator

    Publication Year: 2012 , Page(s): 13 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (627 KB) |  | HTML iconHTML  

    Quantitative analysis of cell dynamic processes through fluorescence microscopy imaging requires simultaneously tracking large and time-varying number of bright spots and its individual states in noisy image sequences. Such process is characterized as a challenging task due to several roadblocks including the severe image noise and clutter, the occlusion of one cell by others, and the weak image contrast. In this paper, we propose a novel ant stochastic searching behavior based tracking algorithm, which is called ANT estimator, to tracking multiple cells in fluorescence image sequences. In our ant system, each ant determines probabilistically potential state and then adjusts its mobility according to cell detection position heuristic information. Simulation results verify the effectiveness of our algorithm when applied to cell tracking cases, and its performance is also compared with the particle filter based cell tracking algorithm. View full abstract»

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  • Performance comparison of a multiple-detection probabilistic data association filter with PCRLB

    Publication Year: 2012 , Page(s): 18 - 23
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (290 KB) |  | HTML iconHTML  

    Most target tracking algorithms assume that at most one measurement is generated by a target in a scan. However, there are tracking problems where this assumption is not valid. For example, multiple detections from a target can arise due to multipath propagation where different signals scattered from a target arrive at the sensor via different paths. With multiple target-originated measurements, most multitarget trackers will fail or become ineffective due to the violation of the one-to-one assumption. For example, the joint probabilistic data association (JPDA) filter is capable of using multiple measurements for a single target through weighted measurement-to-track association, but its fundamental assumption is still one-to-one. In order to rectify this shortcoming, we developed a new algorithm in our previous work, the multiple-detection probabilistic data association (MD-PDA) filter, which is capable of handling multiple detections from a target in a scan, in the presence of false alarm and probability of detection less than unity. In this paper, the performance of this MD-PDA filter is compared with the posterior Cramér-Rao lower bound (PCRLB), which is explicitly derived for the multiple-detection scenario. Furthermore, experimental results show multiple-detection pattern based probabilistic data association improves the state estimation accuracy and reduces the total number of false tracks. View full abstract»

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  • An effective approach for human actions recognition based on optical flow and edge features

    Publication Year: 2012 , Page(s): 24 - 29
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (509 KB) |  | HTML iconHTML  

    Automatic human actions recognition is an interesting and challenging problem, and impacts applications in domain such as human-computer interaction, surveillance, human actions retrieval system and robotics. Deriving an effective actions representation from image sequences is important step for successful action recognition. In this paper, we empirically evaluate actions representation based on statistical global and local features combination, optical flow and edge features, for human action recognition. Firstly, we extract Histogram of Oriented Optical Flow and Spatial Pyramid Histogram of Edge. Secondly, we create the discriminative features by using PCA and LDA. Lastly, we use ANN for actions classification. Our approach is systematically examined on KTH and Weizmann datasets. Extensive experiments illustrate that optical flow and edge features are effective and efficient for actions recognition. We observe our experiments that optical flow and edge features perform efficiently and robustly over a useful of video sequences or camera with static backgrounds, and yield promising performance in video sequences captured in real-world applications such as surveillance and robotics. In addition, we extract action features in 2D using Lucas Kanade and Canny algorithms that have low computational cost. This is feasibility to apply into robotics. View full abstract»

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  • Detecting abnormal activities from multi-sensor surveillance systems

    Publication Year: 2012 , Page(s): 30 - 35
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (775 KB) |  | HTML iconHTML  

    The main purpose of a surveillance system is to monitor valuable assets, such as office buildings and homes, and report any occurring security incidents. Sensor malfunctions or abnormal usages of the system are possible scenarios in a real life and a surveillance system with hundreds of sensors is creating a vast amount of data which is impossible to handle manually. This renders the fixing of these potential faults slow and expensive. This paper proposes a system which can analyse data received from a surveillance system. The proposed system will report abnormal activities, such as malfunctioning or dead sensors, abnormal usage, and abnormal events created by the surveillance system. The experimental evaluation is performed by using six cases describing different types of abnormal activity. The experiments indicate that the proposed system can effectively pinpoint faulty sensors and other abnormal activities. This will ease the task of the maintenance personnel to locate and fix possible problem in the surveillance system. View full abstract»

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  • Sparse Fisher discriminant analysis with Jeffrey's hyperprior

    Publication Year: 2012 , Page(s): 36 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (424 KB) |  | HTML iconHTML  

    The penalty function requires a choice of regularization parameter which controls the degree of parsimony in sparse kernel classifier. This involves an extra parameter apart from kernel parameter in the optimization which must be found via, e.g. cross-validation. This paper introduces a new parsimonious binary kernel Fisher discriminant analysis which does not require a regularization parameter. This can be done by using a Jeffrey's noninformative hyperprior. A Jeffrey's noninformative hyperprior is parameter-free and is adopted through a hierarchical-Bayes interpretation of the Laplacian prior distribution. This leads to a non-requirement of the regularization parameter. The proposed algorithm is compared with other machine learning methods on substantial benchmarks. Moreover, it is also compared with the leading machine learning in virtual screening application. It is found to be less accurate but it is still comparable in a number of cases. View full abstract»

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  • Facial expression representation and classification using 2DPCA

    Publication Year: 2012 , Page(s): 42 - 47
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1007 KB) |  | HTML iconHTML  

    This paper's purpose is to compare the performance between the two feature extraction methods PCA and 2DPCA by applying them to feature extraction stage in the facial expression classification model. To test and evaluate their performance, we performed experiments on two face image databases: JAFFE and YALE. The experimental results also indicated that the performance and speed of 2DPCA are better than PCA in feature extraction capabilities. View full abstract»

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  • Decentralised predictive controllers with parameterised quadratic constraints for nonlinear interconnected systems

    Publication Year: 2012 , Page(s): 48 - 53
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (370 KB) |  | HTML iconHTML  

    A decentralised model predictive control scheme for nonlinear interconnected systems is developed with parameterised stabilising constraints in this paper. Both control and state constraints are inclusive in the problem formulation. An extension to the input-to-state stabilisation framework is given with a newly derived input-to-power-and-state stabilisability (IpSS) condition for interconnected systems. In this work, we consider C1 continuous nonlinear input-affine state-space models with unknown but bounded input disturbance, and develop an LMI-based robust stabilisability condition for the global system. The interactive signals are also unknown and bounded in this development. With an open-loop perspective, the stabilising constraint for model predictive control in this approach is a dynamic quadratic constraint on the initial control vector, which is converted from a dissipation-based constraint using compound output signals. Numerical simulation for three dynamically-coupled subsystems is provided to illustrate the theoretical development. View full abstract»

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  • Nonlinear modelling and identification of a high precision laser tracking system

    Publication Year: 2012 , Page(s): 54 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (423 KB) |  | HTML iconHTML  

    In this paper, the nonlinear modeling and identification of a high precision laser tracking system is presented. In the first part of this study, the authors introduce the experimental set-up as well as the dynamic modeling of the laser tracker system. The derived nonlinear model represents the whole light path of the laser beam, which includes the utilized laser interferometer, the beam deflecting unit as well as the motion of the tracked tool center point. In the second part of this contribution, the parameter identification and the model validation process based on experimental data is presented. Afterwards the authors design a model based MIMO controller, which enables the possibility to decouple the interactive variables. At the end, the effectiveness of the proposed control approach is shown by simulation. View full abstract»

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  • Dissipativity criterion for linear systems with a coupling delay and asymptotically positive realness constraint

    Publication Year: 2012 , Page(s): 60 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (319 KB) |  | HTML iconHTML  

    In this work, a robust stabilizability condition for discrete-time interconnected systems is derived from the quadratically delay-robust dissipativity of the open-loop subsystems and the asymptotically positive realness constraint (APRC) presented previously. Linear interconnections with a single coupling delay element and persistent input disturbances are the essences of this development. A globally robust stabilizable invariant set for the coupling delayed system subject to state and input constraints is considered and incorporated into the quadratic dissipativity criteria. For application, the APRC is subsequently converted into a convex stability constraint for the local MPC optimization. Stability constraints are artificial constraints adding to the optimization problem of MPC solely for the stability assurance purpose. View full abstract»

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  • A transformed descriptor approach to stabilizability of constrained parallel splitting systems

    Publication Year: 2012 , Page(s): 66 - 71
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (384 KB) |  | HTML iconHTML  

    This paper presents the application of the asymptotically positive realness constraint (APRC) in the stabilization for interconnected systems with a descriptor system approach. Handling parallel redundant subsystems that have unknown splitting ratios is an essential part of this development. We have introduced the parallel masking technique in previous work whereby the APRC and the dissipation inequalities are applied to different hierarchies of subsystem interconnections. In this work, the dissipativity criterion for parallel splitting systems is derived from a transformed descriptor system with the employment of a specially structured storage function. The pre-heating an desilication unit operation in an alumina refinery is simulated to illustrate the theoretical results. View full abstract»

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  • Finite-time projective synchronization between two different complex networks

    Publication Year: 2012 , Page(s): 72 - 77
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (400 KB) |  | HTML iconHTML  

    The paper investigates the problem of finite-time projective synchronization between two different complex networks, where two complex networks may be different in the node dynamics, or in the topological structures. By using a finite-time stability theorem and inequality techniques, a sufficient criterion is derived based on Lyapunov stability theory, in the form of linear matrix inequalities (LMIs). The LMIs are readily solved by the LMI toolbox in Matlab. At last, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method. It is worth noting that the coupling configuration matrix is not necessarily symmetric or irreducible; and the inner coupling matrix does not need to be symmetric. View full abstract»

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  • Three-rate neural control of TUAV with coaxial rotor and ducted fan configuration for enhanced situational awareness

    Publication Year: 2012 , Page(s): 78 - 83
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (400 KB) |  | HTML iconHTML  

    This paper describes a critical component of the situational awareness (SA), the control of tactical unmanned aerial vehicle (TUAV) during autonomous flight operations. With the SA strategy, we proposed a three-rate flight control procedure using three autonomous decomposed control subsystems with single NARMA-L2 controller for an unmanned helicopter model with coaxial rotor and ducted fan configuration. This strategy for chosen model of TUAV has been verified by simulation of flight tests using Simulink environment and demonstrated valuable qualities for fast stabilization of TUAV's engines during flight, consequently, fast SA with economy in energy can be asserted during possible missions. View full abstract»

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  • Design and implementation of automatic embedded control hardware and software systems in an unmanned airship

    Publication Year: 2012 , Page(s): 84 - 89
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1039 KB) |  | HTML iconHTML  

    In order to enhance the flexibility, on-demand supply and reasonable cost of geographical probes and observations, we propose an unmanned airship system in conjunction with natural resources probe devices. The use of the unmanned airship creates favorable conditions, increases the efficiency of exploration and supports helpfully the exploitation of natural resources. This is a new approach to meet actual needs. In this paper, an automatic embedded hardware and software system of an unmanned airship are designed and implemented. This work would be a contribution to the research and development of an automatic control and embedded system on a new object. View full abstract»

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  • Loosely coupled GPS/INS integration with Kalman filtering for land vehicle applications

    Publication Year: 2012 , Page(s): 90 - 95
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1079 KB) |  | HTML iconHTML  

    Nowadays, global positioning system (GPS) has been used widely in land vehicles to provide positioning services. However, information from this stand-alone device may be interrupted under some circumstances such as: urban environment, under the tunnels, etc. In addition, low-rate sample time typically 1Hz is another drawback of GPS. Therefore, GPS receiver can be augmented with the inertial navigation system (INS) to provide faster positioning information. By fusing GPS and INS data, the errors are bounded and accuracy increases considerably even when using low-cost INS and GPS. This paper presents a method of INS/GPS integration where a loosely coupled model is formulated and an extended Kalman filter is then applied to estimate information about position, velocity, and acceleration. Experiment results show that the processing time is reduced significantly but the estimated errors are acceptable (less than 1 meters) when applying the proposed integration method under the good signal of GPS. The performance evaluation is also implemented on several road trajectories in urban city that the 3 meters precision could be reached. In addition, accurate positioning and navigation results are still available from 9 to 14 seconds of GPS outages with the position errors spread from 3 to 10 meters (RMS). View full abstract»

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  • Automatic collision avoiding support system for ships in congested waters and at open sea

    Publication Year: 2012 , Page(s): 96 - 101
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1567 KB) |  | HTML iconHTML  

    Ensuring the safety of navigation is always a matter of great importance in the maritime industry. Even with the abundance of navigation aids, marine traffic collisions are still happening, mostly due to human errors in judging traffic information and/or controlling the ship. Our aim in this study is, therefore, to provide an automatic tool for the ship officer in determining the optimal collision avoiding strategy in all prevailing traffic conditions at sea. The route-producing algorithm is an evolutionary one based on Bacteria Foraging Optimization. To improve its performance, an adapting scheme has been employed. A suitable form of the cost function has also been suggested to take into account the traffic rules. It is later shown through simulation studies that the algorithm is suitable, efficient and robust. View full abstract»

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  • A multi-Bernoulli approach to simultaneous segmentation of multiple motions

    Publication Year: 2012 , Page(s): 102 - 107
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (642 KB) |  | HTML iconHTML  

    Most of parametric motion segmentation methods, formulated based on RANSAC technique, are designed to estimate and segment multiple motions in a sequential manner. This paper introduces a new random set theoretical approach to simultaneously estimate the parameters of, and segment multiple motions in a single run. In this approach, the parameters of multiple motions are modelled as a random finite set with multi-Bernoulli distribution. Simulation results involving segmentation of numerous motions show that our method outperforms state-of-art methods in terms of estimation error and correct estimation rate. In addition, it is highly parallelizable and well-suited for implementation by parallel processors. The fast convergence and highly parallelizable nature of the proposed approach make it an excellent choice for real-time estimation and segmentation of multiple motions in computer vision and robotic applications. View full abstract»

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  • A robust algorithm for detection and classification of traffic signs in video data

    Publication Year: 2012 , Page(s): 108 - 113
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB) |  | HTML iconHTML  

    The accurate identification and recognition of the traffic signs is a challenging problem as the developed systems have to address a large number of imaging problems such as motion artifacts, various weather conditions, shadows and partial occlusion, issues that are often encountered in video traffic sequences that are captured from a moving vehicle. These factors substantially degrade the performance of the existing traffic sign recognition (TSR) systems and in this paper we detail the implementation of a new strategy that entails three distinct computational stages. The first component addresses the robust identification of the candidate traffic signs in each frame of the video sequence. The second component discards the traffic sign candidates that do not comply with stringent shape constraints, and the last component implements the classification of the traffic signs using Support Vector Machines (SVMs). The main novel elements of our TSR algorithm are given by the approach that has been developed for traffic sign classification and by the experimental evaluation that was employed to identify the optimal image attributes that are able to maximize the traffic sign classification performance. The TSR algorithm has been validated using video sequences that include the most important categories of signs that are used to regulate the traffic on the Irish and UK roads, and it achieved 87.6% sign detection, 99.2% traffic sign classification accuracy and 86.7% overall traffic sign recognition. View full abstract»

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  • Human action recognition on simple and complex background in video

    Publication Year: 2012 , Page(s): 114 - 119
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (831 KB) |  | HTML iconHTML  

    In this paper, we propose a new method for human action recognition on simple and complex background. There are two approaches: (a) for homogeneous and static background, we propose a combination of HOG3D and HOF to encode the appearance and motion information of human. (b) with realistic video data (including changes of viewpoint, scale, and lighting conditions, partial occlusion of humans and objects, cluttered backgrounds), we exploit the co-occurrence between scene and actions by combining HOG3D and Opponent SIFT to encode action (how) and scene (where) information. The first combination is applied in surveillance videos or videos in which camera placement and parameters are fixed and known. The second one is applied to remained video in which background is not static and cluttered. The experimental results have shown the efficiency and effectiveness of our method. View full abstract»

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  • Two algorithms for detection of mutually occluding traffic signs

    Publication Year: 2012 , Page(s): 120 - 125
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (889 KB) |  | HTML iconHTML  

    The robust identification of the traffic signs represents the first and one of the most important steps in the development of a traffic sign recognition (TSR) system. Traffic signs detection usually involves a color segmentation process that uses the information related to the chromatic properties of the road signs. Since the traffic video data is captured in diverse road and weather conditions, the problem relating to traffic sign detection is quite challenging. Among several issues that need to be addressed during this processing stage, the problem generated by mutually occluding traffic signs (mutual occlusion occurs when one traffic sign partially occludes the surface of other road signs) that are attached to the same pole require special attention. In these situations the color segmentation process fails to correctly identify the regions that are associated with the traffic signs. These traffic sign detection failures compromise the performance of other stages of the TSR system and in this paper we propose two approaches that address the segmentation of mutually occluding traffic signs. The first approach uses the information associated with the inner parts of the traffic signs, while the second approach applies the watershed transform to identify the signs that have their borders in contact or are mutually occluding. View full abstract»

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